OPTES: A Tool for Behavior-based Student Programming Progress Estimation

Programming ability is the core ability for students in computer majors, but current training in programming ability lacks process observation and evaluation. Relying solely on a small number of tests is insufficient in evaluating students’ performance. A system that tracks and evaluates the programming process is needed to estimate students’ performance, effort, and emotional states. Teachers can improve courses, identify and assist students who encounter difficulties based on that system. This paper presents a web-based Online Programming Training Estimation System (OPTES) that provides an integrated environment for after-class programming assignments, which is designed to track and estimate the students’ learning status continuously over a long period, e.g., a whole semester. Beyond normal functions like Online Judge systems, OPTES collects and analyzes students’ progress data, estimates students’ emotional states based on machine learning technologies, and visualizes these data intuitively. Correlation analysis shows an evident correlation between progress data, emotional states, and performance. This system has been successfully deployed and evaluated empirically for two semesters, used by over 500 sophomores, which also received positive feedback from the teachers who use the system.